Machine Learning-Based Investigation of the 3D Printer Cooling Effect on Print Quality in Fused Filament Fabrication: A Cybersecurity Perspective

Abstract

Additive manufacturing (AM), also known as three-dimensional (3D) printing, is the process of building a solid object in a layer-wise manner. Cybersecurity is a prevalent issue that appears more and more frequently as AM becomes popular. This paper focuses on the effect of fan speed on the printing quality and presents a plugin called Fan Speed Attack Detection (FSAD) that predicts and monitors fan speeds throughout the printing process. The goal of the plugin is to prevent cybersecurity attacks, specifically targeting fan speed. Using the proposed FSAD, any fan speed changes during the printing process are evaluated to see whether the printer can sustain the abnormal fan speed change and still maintain good print quality.

Keywords

• additive manufacturing
• machine learning
• cybersecurity
• fan speed